library(ggplot2)
library(readxl)
library(readr)
library(tidyverse)
library(dplyr)
library(sf)
library(plotly)
library(geojsonio)
library(knitr)
library(tidyr)
# Load the dataset
df_descriptive=read_csv("data_final.csv")
data_final <- read_csv("data_final.csv")

Time-Based Exploratory Data Analysis

Shooting Incidents by Year in NYC

# Grouping the data by year to count the number of incidents
df_descriptive$Year <- as.factor(df_descriptive$Year)
year_counts <- as.data.frame(table(df_descriptive$Year))
colnames(year_counts) <- c('Year', 'Count')


# Plotting the line chart for shooting incidents by year with data points labeled
ggplot(year_counts, aes(x = Year, y = Count, group = 1)) +
  geom_line(color = 'blue') +
  geom_point(size = 3) +
  geom_text(aes(label = Count), vjust = -0.5, size = 3) +
  labs(title = 'Shooting Incidents by Year in NYC',
       x = 'Year',
       y = 'Number of Shooting Incidents') +
  theme_minimal()

The chart shows a stable trend in shooting incidents from 2017 to 2019, followed by a sharp increase in 2020, likely linked to socio-economic factors like the COVID-19 pandemic. Incidents peaked in 2021 and then declined through 2023.

Bar Chart of Shooting Incidents by Month in NYC

# Grouping the data by month
df_descriptive$Month <- as.factor(df_descriptive$Month)
month_counts <- as.data.frame(table(df_descriptive$Month))
colnames(month_counts) <- c('Month', 'Count')

# Plotting the bar chart for shooting incidents by month
ggplot(month_counts, aes(x = Month, y = Count)) +
  geom_bar(stat = 'identity', fill = 'skyblue') +
  geom_text(aes(label = Count), vjust = -0.5) +
  labs(title = 'Bar Chart of Shooting Incidents by Month in NYC',
       x = 'Month',
       y = 'Number of Shooting Incidents') +
  theme_minimal()

The bar chart shows a seasonal trend in shooting incidents in NYC, with a significant increase during the warmer months, peaking in July (1248 incidents). The highest numbers are seen in June, July, and August, while the lowest are in February (494 incidents) and January (617 incidents), suggesting fewer incidents during colder months. The gradual rise from January to July, followed by a decline towards December, indicates a correlation between warmer weather and increased incidents, potentially due to increased outdoor activities.

Density Plot of Shooting Incident Occurrence Time in NYC

# Convert OCCUR_TIME to a proper time format
df_descriptive$OCCUR_TIME <- format(strptime(df_descriptive$OCCUR_TIME, format = "%H:%M:%S"), "%H:%M:%S")

# Plotting the density of the occurrence time
ggplot(df_descriptive, aes(x = as.POSIXct(OCCUR_TIME, format = "%H:%M:%S"))) +
  geom_density(fill = 'skyblue', alpha = 0.6) +
  labs(title = 'Density Plot of Shooting Incident Occurrence Time in NYC',
       x = 'Time of Occurrence',
       y = 'Density') +
  scale_x_datetime(date_labels = "%H:%M") +
  theme_minimal()

The density plot shows changes in shooting incident trends throughout the day in NYC, with two distinct peaks. The first peak occurs in the late night to early morning hours, and the second peak appears in the evening. The lowest density is observed in the late morning to early afternoon.

Comparison of Shooting Incidents During Daytime and Nighttime

sky_dark_counts <- df_descriptive %>%
  group_by(Sky_Is_Dark) %>%
  summarise(total_incidents = n())

ggplot(sky_dark_counts, aes(x = Sky_Is_Dark, y = total_incidents, fill = Sky_Is_Dark)) +
  geom_bar(stat = 'identity') +
  scale_x_discrete(labels = c("FALSE" = "Daytime", "TRUE" = "Nighttime")) + # Update x-axis labels
  scale_fill_manual(values = c("FALSE" = "skyblue", "TRUE" = "darkblue"),
                    labels = c("FALSE" = "Daytime", "TRUE" = "Nighttime")) +
  labs(title = 'Number of Shooting Incidents by time (Daytime vs. Nigttime)',
       x = 'Time of a day(Daytime vs. Nigttime)',
       y = 'Number of Shooting Incidents') +
  theme_minimal()

The number of shooting incidents when the sky was dark is significantly higher than when it was bright. This might indicate that shootings are more likely to occur during nighttime or low visibility conditions. The higher number of incidents during dark conditions could be due to factors such as reduced visibility, higher activity at night, or fewer people around, making it easier for incidents to occur undetected.

Map of Number of incidents by NYC BOROs

Process of data manipulation

# Load dataset

cdta_shape = st_read("nycdta2020_24d/nycdta2020.shp")
## Reading layer `nycdta2020' from data source 
##   `D:\Rproject_ds1\ZebangZHANGstat.github.io\nycdta2020_24d\nycdta2020.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 71 features and 8 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 913175.1 ymin: 120128.4 xmax: 1067383 ymax: 272844.3
## Projected CRS: NAD83 / New York Long Island (ftUS)
boro_shape = st_read("Borough Boundaries/geo_export_391a75ed-0ae4-4c88-8c30-3588c75bd01e.shp")
## Reading layer `geo_export_391a75ed-0ae4-4c88-8c30-3588c75bd01e' from data source `D:\Rproject_ds1\ZebangZHANGstat.github.io\Borough Boundaries\geo_export_391a75ed-0ae4-4c88-8c30-3588c75bd01e.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 5 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -74.25559 ymin: 40.49613 xmax: -73.70001 ymax: 40.91553
## Geodetic CRS:  WGS 84
# Count the boro incident
boro_incident_counts <- data_final %>%
  group_by(BORO) %>%
  summarise(Number_of_Incidents = n(), .groups = "drop")  %>%
  mutate(BORO = tolower(BORO) )

# Lowercase the boro in boro_shape
boro_shape = boro_shape %>%
  mutate(boro_name = tolower(boro_name))
 

# Merge spatial data with incident counts
boro_map_data <- boro_shape %>%
  left_join(boro_incident_counts, by = c("boro_name" = "BORO"))

Plot of Total Number of Incidents Across NYC BOROs (2017-2023)

data_final <- read_csv("data_final.csv")
boro_map_data <- boro_map_data %>%
  mutate(
    hover_text = paste("Borough:", boro_name, "<br>Total Incidents:", Number_of_Incidents)
  )

# Create the interactive plot with click functionality
plot <- plot_ly(
  data = boro_map_data,
  type = "scattermapbox",
  split = ~boro_name,  # Separate polygons by boroughs
  color = ~Number_of_Incidents,  # Color based on the number of incidents
  colors = "viridis",  # Use a color scale
  text = ~hover_text,  # Display hover text
  hoverinfo = "text",
  marker = list(size = 8, opacity = 0.7)
) %>%
  layout(
    title = "Total Number of Incidents Across NYC BOROs (2017-2023)",
    mapbox = list(
      style = "carto-positron",  # Base map style
      center = list(lon = -74.00, lat = 40.7128),  # Center map on NYC
      zoom = 9
    )
  )

# Add click functionality to display the borough name and number of incidents
plot <- plot %>%
  event_register("plotly_click") %>%
  htmlwidgets::onRender("
    function(el, x) {
      el.on('plotly_click', function(d) {
        var point = d.points[0];
        var text = point.text;
        alert('You clicked on: ' + text);
      });
    }
  ")

# Display the interactive plot
plot

The map shows the total number of incidents across NYC boroughs (2017–2023), with each borough represented by a distinct color. A gradient is used to indicate the magnitude of incidents, with brighter shades corresponding to higher counts, ranging from 0 to over 3,000 incidents. Each borough is outlined and filled with its respective color, making it easy to distinguish. The legend on the right identifies the boroughs (Bronx, Brooklyn, Manhattan, Queens, Staten Island) and aligns with the color gradient to show the number of incidents. This visualization highlights geographical disparities in incident frequency across the boroughs, aiding in understanding spatial distribution patterns.

Map of Number of incidents by NYC CDTAs

Process of data manipulation

## There is space between letter and number in CDTA, delete the space below
data_final$CDTA <- gsub(" ", "", data_final$CDTA)

cdta_incident_counts <- data_final %>%
  group_by(CDTA) %>%
  summarise(Number_of_Incidents = n(), .groups = "drop")

# Remove any trailing spaces or mismatches in CDTA identifiers:
cdta_shape$CDTA2020 <- gsub(" ", "", cdta_shape$CDTA2020)
data_final$CDTA <- gsub(" ", "", data_final$CDTA)

# Identify Missing Matches
unmatched_cdta <- setdiff(cdta_shape$CDTA2020, data_final$CDTA)

#Re-Merge the Data
cdta_map_data <- cdta_shape %>%
  left_join(cdta_incident_counts, by = c("CDTA2020" = "CDTA"))

# Update NA Handling
cdta_map_data <- cdta_map_data %>%
  mutate(
    Number_of_Incidents = ifelse(is.na(Number_of_Incidents), 0, Number_of_Incidents),
    Incident_Range = cut(
      Number_of_Incidents,
      breaks = seq(0, 600, by = 120), 
      labels = c("0-120", "121-240", "241-360", "361-480", "481-600"),
      include.lowest = TRUE
    )
  )

“QN80” “SI95” “QN84” are NAs. Therefore, these 3 CDTAs are assigned with 0 incident.

Plot of Total Number of Incidents Across NYC CDTAs from 2017 to 2023

ggplot(data = cdta_map_data) +
  geom_sf(aes(fill = Incident_Range), color = "white", size = 0.2) +
 geom_sf_text(aes(label = Number_of_Incidents), size = 3, color = "black") +  # Add labels+
  scale_fill_manual(
    values = c(
      "0-120" = "#b2e2e2",
      "121-240" = "skyblue",
      "241-360" = "#66c2a4",
      "361-480" = "#2ca25f",
      "481-600" = "#006d2c"
    ),
    name = "Number of Incidents"
  ) +
  labs(
    title = "Total Number of Incidents Across NYC CDTAs from 2017 to 2023",
    subtitle = "Incidents grouped by range (0-600, 120 breaks)"
  ) +
  theme_minimal() +
  theme(
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    panel.grid = element_blank(),
    axis.title.x = element_blank(),
    axis.title.y = element_blank()
  )

The map shows NYC CDTA incidents (2017–2023) using a gradient from light blue (fewer incidents) to dark green (more incidents), with counts labeled on each district. Dark green areas highlight hotspots, likely in densely populated regions, while lighter blue areas, like Staten Island, show fewer incidents. This visualization helps identify trends and prioritize safety efforts.

Map of CDTA Incidents in each Borough from 2017 to 2023

boroughs <- unique(cdta_map_data$BoroName)
for (b in boroughs) {
    borough_data <- cdta_map_data %>%
        filter(BoroName == b)
    plot <- ggplot(data = borough_data) +
        geom_sf(aes(fill = Number_of_Incidents), color = "black") +
      geom_sf_text(aes(label = Number_of_Incidents), size = 3, color = "black") +  # Add labels
        scale_fill_gradientn(
      colors = c("skyblue","green", "yellow", "red"), # Custom color scale
      name = "Number of Incidents"
    ) +
        labs(
            title = paste("CDTA Incidents in", b),
            subtitle = "2017 to 2023",
            x = "Longitude",
            y = "Latitude"
        ) +
        theme_minimal()
    print(plot) 
}

The Bronx demonstrates significant variability in the number of incidents across its CDTAs, with central and southern regions experiencing the highest numbers of incidents, some exceeding 300. This highlights the borough as a hotspot for crime relative to others. Socioeconomic challenges, high population density, or structural inequities could contribute to these trends. Interventions should prioritize these high-incident areas by allocating additional law enforcement resources and community support programs to mitigate the causes of elevated crime levels.

Manhattan shows a relatively low overall number of incidents, except for a few northern neighborhoods, where one CDTA is a clear outlier with incidents surpassing 1200. This disparity suggests a concentration of crime in specific areas rather than borough-wide. The localized nature of the issue could be due to unique socioeconomic pressures or demographic factors in those neighborhoods. Targeted initiatives such as community policing or economic investment in northern Manhattan could help address these localized hotspots.

Queens generally exhibits a safer profile with most CDTAs reporting fewer than 100 incidents. However, one central area stands out with more than 400 incidents, marking it as a concern. The low number of incidents elsewhere reflects the suburban and less dense nature of the borough. Efforts in Queens should focus on analyzing the central hotspot to determine the underlying causes, such as potential economic or social stressors, and implementing programs to improve community safety.

Brooklyn presents a widespread distribution of incidents, with several CDTAs exceeding 500 incidents. The borough shows higher overall numbers compared to Queens and Manhattan, which may be linked to its high population density and mixed socioeconomic profile. The more even distribution of incidents suggests a need for borough-wide interventions rather than focusing on isolated areas. A combination of public safety initiatives, housing improvements, and community programs would be essential for addressing crime across the borough.

Staten Island stands out with the lowest number of incidents among the five boroughs. Most CDTAs report fewer than 50 incidents, with one area as an exception, exceeding 200 incidents. This aligns with Staten Island’s suburban and less densely populated character, which likely contributes to its lower crime rates. Maintaining this safety level requires continued focus on community engagement and ensuring sufficient resources are available to address any emerging hotspots.

Geographic EDA by NTA and Borough

Exploratory Data Analysis by Neighborhood Tabulation Area

Table showing Top 10 NTAs with the Highest Total Shooting Incidents Number (2017-2023)

# Table showing Top 10 NTAs with the Highest Total Shooting Incidents
top_10_nta_overall <- df_descriptive %>%
  group_by(BORO, NTA) %>%
  summarise(total_incidents = n(),
            total_population = first(Total_population_nta),
            percent_poverty = first(Percent_poverty),
            percent_education = first(Percent_education)) %>%
  arrange(desc(total_incidents)) %>%
  ungroup() %>%
  slice(1:10) %>%
  select(NTA, BORO, total_incidents, total_population, percent_poverty, percent_education)
# Display the table in the desired format
kable(top_10_nta_overall, digits = 2, caption = "Top 10 NTAs with the Highest Total Shooting Incidents (2017-2023)")
Top 10 NTAs with the Highest Total Shooting Incidents (2017-2023)
NTA BORO total_incidents total_population percent_poverty percent_education
Brownsville BROOKLYN 350 60470 37.2 76.9
Bedford-Stuyvesant (East) BROOKLYN 323 84653 22.2 87.8
Harlem (North) MANHATTAN 253 83327 25.1 82.9
Crown Heights (North) BROOKLYN 235 85275 20.5 84.1
East Harlem (North) MANHATTAN 229 64655 32.3 75.9
Mott Haven-Port Morris BRONX 207 57718 43.7 62.0
Bedford-Stuyvesant (West) BROOKLYN 207 89189 26.0 84.8
East New York-New Lots BROOKLYN 205 53004 25.3 82.6
Concourse-Concourse Village BRONX 169 69387 31.9 70.5
Mount Eden-Claremont (West) BRONX 166 49651 31.8 67.9

Table showing Top 10 NTAs with the Highest Annual Incident Rate

# Get the Top 10 NTAs with the Highest Incident Rate by Year
top_10_incident_rate <- df_descriptive %>%
  distinct(incident_rate_by_year_nta, .keep_all = TRUE) %>%
  filter(NTAType == 'Residential') %>%
  arrange(desc(incident_rate_by_year_nta)) %>%
  slice(1:10)

# Pull information for the Top 10 NTAs
top_10_nta_incident_rate <- top_10_incident_rate %>%
  select(NTA, BORO,Year,incident_rate = incident_rate_by_year_nta,
         total_population = Total_population_nta,
         percent_poverty = Percent_poverty,
         percent_education = Percent_education)

# Display the table in the desired format
kable(top_10_nta_incident_rate, digits = 5, caption = "Top 10 NTAs with the Highest Incident Rate in Each Borough")
Top 10 NTAs with the Highest Incident Rate in Each Borough
NTA BORO Year incident_rate total_population percent_poverty percent_education
Claremont Village-Claremont (East) BRONX 2021 0.18735 24553 43.2 61.7
Brownsville BROOKLYN 2020 0.15380 60470 37.2 76.9
Tremont BRONX 2020 0.11820 32150 36.3 71.4
Inwood MANHATTAN 2021 0.11571 36299 15.3 77.0
East New York-New Lots BROOKLYN 2020 0.10754 53004 25.3 82.6
Hunts Point BRONX 2021 0.10574 15131 34.3 72.2
Mount Eden-Claremont (West) BRONX 2021 0.10070 49651 31.8 67.9
Brownsville BROOKLYN 2022 0.09922 60470 37.2 76.9
Brownsville BROOKLYN 2021 0.09757 60470 37.2 76.9
Tremont BRONX 2023 0.09642 32150 36.3 71.4

Table showing Top 10 total incident NTA (2017-2023) in Each Borough

# Table showing Top 10 total incident NTA in Each Borough
incident_by_nta_borough <- df_descriptive %>%
  drop_na() %>%
  group_by(BORO, NTA) %>%
  summarise(total_incidents = n()) %>%
  arrange(BORO, desc(total_incidents)) %>%
  group_by(BORO) %>%
  slice_max(n = 10, order_by = total_incidents) %>%
  select(-total_incidents) %>%
  group_by(BORO) %>%
  mutate(row_num = row_number()) %>%
  pivot_wider(names_from = BORO, values_from = NTA) %>%
  unnest(cols = c(BRONX, BROOKLYN, MANHATTAN, QUEENS, `STATEN ISLAND`)) %>%
  select(row_num, everything())%>%
  slice(1:10)

# Display the table in the desired format
kable(incident_by_nta_borough, caption = "Top 10 NTAs with Total Shooting Incidents in Each Borough (2017-2023)")
Top 10 NTAs with Total Shooting Incidents in Each Borough (2017-2023)
row_num BRONX BROOKLYN MANHATTAN QUEENS STATEN ISLAND
1 Mott Haven-Port Morris Brownsville East Harlem (North) Far Rockaway-Bayswater St. George-New Brighton
2 Mount Hope Bedford-Stuyvesant (East) Harlem (North) Baisley Park Tompkinsville-Stapleton-Clifton-Fox Hills
3 Tremont Crown Heights (North) East Harlem (South) Richmond Hill West New Brighton-Silver Lake-Grymes Hill
4 Mount Eden-Claremont (West) East New York-New Lots Harlem (South) Jamaica Mariner’s Harbor-Arlington-Graniteville
5 Concourse-Concourse Village East Flatbush-Remsen Village Washington Heights (South) Rockaway Beach-Arverne-Edgemere Annadale-Huguenot-Prince’s Bay-Woodrow
6 Williamsbridge-Olinville East New York (North) Chelsea-Hudson Yards St. Albans Port Richmond
7 Melrose Bedford-Stuyvesant (West) Hamilton Heights-Sugar Hill South Ozone Park Rosebank-Shore Acres-Park Hill
8 Belmont Canarsie Inwood Elmhurst Grasmere-Arrochar-South Beach-Dongan Hills
9 Longwood Coney Island-Sea Gate Chinatown-Two Bridges South Jamaica Great Kills-Eltingville
10 Fordham Heights East New York-City Line Washington Heights (North) Astoria (East)-Woodside (North) New Dorp-Midland Beach

Top 10 NTAs with the Highest Total Shooting Incidents in each boro & Top 10 NTAs with the Highest Annual Incident Rate in each boro

MANHATTAN

Table showing Top 10 NTAs with the Highest Total Shooting Incidents (2017-2023) in MANHATTAN

top_10_nta_overall_manhattan <- df_descriptive %>%
  filter(BORO == 'MANHATTAN') %>% 
  group_by(BORO, NTA) %>%
  summarise(total_incidents = n(),
            total_population = first(Total_population_nta),
            percent_poverty = first(Percent_poverty),
            percent_education = first(Percent_education)) %>%
  arrange(desc(total_incidents)) %>%
  ungroup() %>%
  slice(1:10) %>%
  select(NTA, total_incidents, total_population, percent_poverty, percent_education)
# Display the table in the desired format
kable(top_10_nta_overall_manhattan, digits = 2, caption = "Top 10 NTAs with the Highest Total Shooting Incidents in MANHATTAN (2017-2023)")
Top 10 NTAs with the Highest Total Shooting Incidents in MANHATTAN (2017-2023)
NTA total_incidents total_population percent_poverty percent_education
Harlem (North) 253 83327 25.1 82.9
East Harlem (North) 229 64655 32.3 75.9
East Harlem (South) 145 59814 29.0 78.3
Harlem (South) 130 47113 18.8 87.4
Inwood 105 36299 15.3 77.0
Washington Heights (South) 88 72037 19.9 70.3
Washington Heights (North) 67 71842 14.8 79.3
Hamilton Heights-Sugar Hill 54 49410 24.0 78.6
Manhattanville-West Harlem 46 22183 26.7 77.4
East Village 41 71436 24.3 87.7

Table showing Top 10 NTAs with the Highest Annual Shooting Incident Rate in MANHATTAN

top_10_incident_rate_manhattan <- df_descriptive %>%
  distinct(incident_rate_by_year_nta, .keep_all = TRUE) %>%
  filter(BORO == 'MANHATTAN') %>% 
  filter(NTAType == 'Residential') %>%
  arrange(desc(incident_rate_by_year_nta)) %>%
  slice(1:10)

# Pull information for the Top 10 NTAs
top_10_nta_incident_rate_manhattan <- top_10_incident_rate_manhattan %>%
  select(NTA, Year, incident_rate = incident_rate_by_year_nta,
         total_population = Total_population_nta,
         percent_poverty = Percent_poverty,
         percent_education = Percent_education)

# Display the table in the desired format
kable(top_10_nta_incident_rate_manhattan, digits = 6, caption = "Top 10 NTAs with the Highest Annual Incident Rate in MANHATTAN")
Top 10 NTAs with the Highest Annual Incident Rate in MANHATTAN
NTA Year incident_rate total_population percent_poverty percent_education
Inwood 2021 0.115706 36299 15.3 77.0
East Harlem (North) 2022 0.086614 64655 32.3 75.9
Harlem (North) 2021 0.080406 83327 25.1 82.9
Inwood 2020 0.068872 36299 15.3 77.0
Harlem (South) 2021 0.065799 47113 18.8 87.4
Harlem (North) 2022 0.064805 83327 25.1 82.9
East Harlem (North) 2021 0.061867 64655 32.3 75.9
Harlem (South) 2022 0.057309 47113 18.8 87.4
Harlem (North) 2020 0.055204 83327 25.1 82.9
East Harlem (South) 2021 0.055171 59814 29.0 78.3

BROOKLYN

Table showing Top 10 NTAs with the Highest Total Shooting Incidents (2017-2023) in BROOKLYN

top_10_nta_overall_brooklyn <- df_descriptive %>%
  filter(BORO == 'BROOKLYN') %>% 
  group_by(BORO, NTA) %>%
  summarise(total_incidents = n(),
            total_population = first(Total_population_nta),
            percent_poverty = first(Percent_poverty),
            percent_education = first(Percent_education)) %>%
  arrange(desc(total_incidents)) %>%
  ungroup() %>%
  slice(1:10) %>%
  select(NTA, total_incidents, total_population, percent_poverty, percent_education)
# Display the table in the desired format
kable(top_10_nta_overall_brooklyn, digits = 2, caption = "Top 10 NTAs with the Highest Total Shooting Incidents in BROOKLYN (2017-2023)")
Top 10 NTAs with the Highest Total Shooting Incidents in BROOKLYN (2017-2023)
NTA total_incidents total_population percent_poverty percent_education
Brownsville 350 60470 37.2 76.9
Bedford-Stuyvesant (East) 323 84653 22.2 87.8
Crown Heights (North) 235 85275 20.5 84.1
Bedford-Stuyvesant (West) 207 89189 26.0 84.8
East New York-New Lots 205 53004 25.3 82.6
Canarsie 166 89932 13.0 88.1
East New York (North) 148 42818 25.5 79.4
East Flatbush-Remsen Village 126 39115 18.7 83.7
Ocean Hill 120 37952 27.3 83.1
Flatbush 111 66503 13.7 87.8

Table showing Top 10 NTAs with the Highest Annual Shooting Incident Rate in BROOKLYN

top_10_incident_rate_brooklyn <- df_descriptive %>%
  distinct(incident_rate_by_year_nta, .keep_all = TRUE) %>%
  filter(BORO == 'BROOKLYN') %>% 
  filter(NTAType == 'Residential') %>%
  arrange(desc(incident_rate_by_year_nta)) %>%
  slice(1:10)

# Pull information for the Top 10 NTAs
top_10_nta_incident_rate_brooklyn <- top_10_incident_rate_brooklyn %>%
  select(NTA, Year, incident_rate = incident_rate_by_year_nta,
         total_population = Total_population_nta,
         percent_poverty = Percent_poverty,
         percent_education = Percent_education)

# Display the table in the desired format
kable(top_10_nta_incident_rate_brooklyn, digits = 6, caption = "Top 10 NTAs with the Annual Highest Incident Rate in BROOKLYN")
Top 10 NTAs with the Annual Highest Incident Rate in BROOKLYN
NTA Year incident_rate total_population percent_poverty percent_education
Brownsville 2020 0.153795 60470 37.2 76.9
East New York-New Lots 2020 0.107539 53004 25.3 82.6
Brownsville 2022 0.099223 60470 37.2 76.9
Brownsville 2021 0.097569 60470 37.2 76.9
Bedford-Stuyvesant (East) 2020 0.088597 84653 22.2 87.8
East New York (North) 2020 0.079406 42818 25.5 79.4
East New York-New Lots 2021 0.079239 53004 25.3 82.6
East Flatbush-Remsen Village 2020 0.071584 39115 18.7 83.7
Ocean Hill 2021 0.071142 37952 27.3 83.1
Brownsville 2018 0.069456 60470 37.2 76.9

BRONX

Table showing Top 10 NTAs with the Highest Total Shooting Incidents (2017-2023) in BRONX

top_10_nta_overall_bronx <- df_descriptive %>%
  filter(BORO == 'BRONX') %>% 
  group_by(BORO, NTA) %>%
  summarise(total_incidents = n(),
            total_population = first(Total_population_nta),
            percent_poverty = first(Percent_poverty),
            percent_education = first(Percent_education)) %>%
  arrange(desc(total_incidents)) %>%
  ungroup() %>%
  slice(1:10) %>%
  select(NTA, total_incidents, total_population, percent_poverty, percent_education)
# Display the table in the desired format
kable(top_10_nta_overall_bronx, digits = 2, caption = "Top 10 NTAs with the Highest Total Shooting Incidents in BRONX (2017-2023)")
Top 10 NTAs with the Highest Total Shooting Incidents in BRONX (2017-2023)
NTA total_incidents total_population percent_poverty percent_education
Mott Haven-Port Morris 207 57718 43.7 62.0
Concourse-Concourse Village 169 69387 31.9 70.5
Mount Eden-Claremont (West) 166 49651 31.8 67.9
Williamsbridge-Olinville 158 61346 21.0 76.6
Melrose 152 42651 40.7 68.2
Mount Hope 149 49099 30.2 66.8
Tremont 138 32150 36.3 71.4
Longwood 130 40289 32.0 66.9
Belmont 116 35825 39.6 65.6
Fordham Heights 112 32099 33.2 66.0

Table showing Top 10 NTAs with the Highest Annual Shooting Incident Rate in BRONX

top_10_incident_rate_bronx <- df_descriptive %>%
  distinct(incident_rate_by_year_nta, .keep_all = TRUE) %>%
  filter(BORO == 'BRONX') %>% 
  filter(NTAType == 'Residential') %>%
  arrange(desc(incident_rate_by_year_nta)) %>%
  slice(1:10)

# Pull information for the Top 10 NTAs
top_10_nta_incident_rate_bronx <- top_10_incident_rate_bronx %>%
  select(NTA, Year, incident_rate = incident_rate_by_year_nta,
         total_population = Total_population_nta,
         percent_poverty = Percent_poverty,
         percent_education = Percent_education)

# Display the table in the desired format
kable(top_10_nta_incident_rate_bronx, digits = 6, caption = "Top 10 NTAs with the Highest Annual Incident Rate in BRONX")
Top 10 NTAs with the Highest Annual Incident Rate in BRONX
NTA Year incident_rate total_population percent_poverty percent_education
Claremont Village-Claremont (East) 2021 0.187350 24553 43.2 61.7
Tremont 2020 0.118196 32150 36.3 71.4
Hunts Point 2021 0.105743 15131 34.3 72.2
Mount Eden-Claremont (West) 2021 0.100703 49651 31.8 67.9
Tremont 2023 0.096423 32150 36.3 71.4
West Farms 2021 0.094307 20147 44.8 61.1
Longwood 2021 0.091836 40289 32.0 66.9
Mount Hope 2023 0.089615 49099 30.2 66.8
West Farms 2022 0.084380 20147 44.8 61.1
Morrisania 2020 0.082431 37607 35.7 65.9

QUEENS

Table showing Top 10 NTAs with the Highest Total Shooting Incidents (2017-2023) in QUEENS

top_10_nta_overall_queens <- df_descriptive %>%
  filter(BORO == 'QUEENS') %>% 
  group_by(BORO, NTA) %>%
  summarise(total_incidents = n(),
            total_population = first(Total_population_nta),
            percent_poverty = first(Percent_poverty),
            percent_education = first(Percent_education)) %>%
  arrange(desc(total_incidents)) %>%
  ungroup() %>%
  slice(1:10) %>%
  select(NTA, total_incidents, total_population, percent_poverty, percent_education)
# Display the table in the desired format
kable(top_10_nta_overall_queens, digits = 2, caption = "Top 10 NTAs with the Highest Total Shooting Incidents in QUEENS (2017-2023)")
Top 10 NTAs with the Highest Total Shooting Incidents in QUEENS (2017-2023)
NTA total_incidents total_population percent_poverty percent_education
Far Rockaway-Bayswater 116 58648 18.3 79.9
Rockaway Beach-Arverne-Edgemere 100 41367 22.1 82.1
St. Albans 93 51816 8.6 88.8
South Jamaica 91 44401 13.4 78.9
Baisley Park 83 43090 11.4 83.5
Jamaica 82 60993 15.7 70.4
Queensbridge-Ravenswood-Dutch Kills 79 32954 16.5 85.0
South Ozone Park 66 79540 9.7 76.7
Richmond Hill 55 34100 10.6 79.8
Laurelton 45 26088 5.4 91.2

Table showing Top 10 NTAs with the Highest Annual Shooting Incident Rate in QUEENS

top_10_incident_rate_queens <- df_descriptive %>%
  distinct(incident_rate_by_year_nta, .keep_all = TRUE) %>%
  filter(BORO == 'QUEENS') %>% 
  filter(NTAType == 'Residential') %>%
  arrange(desc(incident_rate_by_year_nta)) %>%
  slice(1:10)

# Pull information for the Top 10 NTAs
top_10_nta_incident_rate_queens <- top_10_incident_rate_queens %>%
  select(NTA, Year, incident_rate = incident_rate_by_year_nta,
         total_population = Total_population_nta,
         percent_poverty = Percent_poverty,
         percent_education = Percent_education)

# Display the table in the desired format
kable(top_10_nta_incident_rate_queens, digits = 6, caption = "Top 10 NTAs with the Highest Annual Incident Rate in QUEENS")
Top 10 NTAs with the Highest Annual Incident Rate in QUEENS
NTA Year incident_rate total_population percent_poverty percent_education
South Jamaica 2020 0.069818 44401 13.4 78.9
Rockaway Beach-Arverne-Edgemere 2020 0.067687 41367 22.1 82.1
Far Rockaway-Bayswater 2020 0.064793 58648 18.3 79.9
Queensbridge-Ravenswood-Dutch Kills 2020 0.054622 32954 16.5 85.0
Laurelton 2020 0.053665 26088 5.4 91.2
Rockaway Beach-Arverne-Edgemere 2021 0.050765 41367 22.1 82.1
Richmond Hill 2023 0.046921 34100 10.6 79.8
Queensbridge-Ravenswood-Dutch Kills 2019 0.045518 32954 16.5 85.0
St. Albans 2021 0.044388 51816 8.6 88.8
Richmond Hill 2022 0.043988 34100 10.6 79.8

STATEN ISLAND

Table showing Top 10 NTAs with the Highest Total Shooting Incidents (2017-2023) in STATEN ISLAND

top_10_nta_overall_staten <- df_descriptive %>%
  filter(BORO == 'STATEN ISLAND') %>% 
  group_by(BORO, NTA) %>%
  summarise(total_incidents = n(),
            total_population = first(Total_population_nta),
            percent_poverty = first(Percent_poverty),
            percent_education = first(Percent_education)) %>%
  arrange(desc(total_incidents)) %>%
  ungroup() %>%
  slice(1:10) %>%
  select(NTA, total_incidents, total_population, percent_poverty, percent_education)
# Display the table in the desired format
kable(top_10_nta_overall_staten, digits = 2, caption = "Top 10 NTAs with the Highest Total Shooting Incidents in STATEN ISLAND (2017-2023)")
Top 10 NTAs with the Highest Total Shooting Incidents in STATEN ISLAND (2017-2023)
NTA total_incidents total_population percent_poverty percent_education
St. George-New Brighton 48 20549 18.5 79.9
Mariner’s Harbor-Arlington-Graniteville 46 33492 16.8 83.3
Tompkinsville-Stapleton-Clifton-Fox Hills 42 19027 25.5 79.2
Rosebank-Shore Acres-Park Hill 34 25510 14.4 81.7
West New Brighton-Silver Lake-Grymes Hill 26 37010 9.9 91.2
Port Richmond 23 22609 22.0 83.2
Grasmere-Arrochar-South Beach-Dongan Hills 8 36259 11.3 85.4
Annadale-Huguenot-Prince’s Bay-Woodrow 6 40534 6.0 92.6
New Dorp-Midland Beach 5 29083 8.5 84.6
Arden Heights-Rossville 4 30683 4.3 92.3

Table showing Top 10 NTAs with the Highest Annual Shooting Incident Rate in STATEN ISLAND

top_10_incident_rate_staten <- df_descriptive %>%
  distinct(incident_rate_by_year_nta, .keep_all = TRUE) %>%
  filter(BORO == 'STATEN ISLAND') %>% 
  filter(NTAType == 'Residential') %>%
  arrange(desc(incident_rate_by_year_nta)) %>%
  slice(1:10)

# Pull information for the Top 10 NTAs
top_10_nta_incident_rate_staten <- top_10_incident_rate_staten %>%
  select(NTA, Year, incident_rate = incident_rate_by_year_nta,
         total_population = Total_population_nta,
         percent_poverty = Percent_poverty,
         percent_education = Percent_education)

# Display the table in the desired format
kable(top_10_nta_incident_rate_staten, digits = 6, caption = "Top 10 NTAs with the Highest Annual Incident Rate in STATEN ISLAND")
Top 10 NTAs with the Highest Annual Incident Rate in STATEN ISLAND
NTA Year incident_rate total_population percent_poverty percent_education
St. George-New Brighton 2023 0.058397 20549 18.5 79.9
Tompkinsville-Stapleton-Clifton-Fox Hills 2021 0.052557 19027 25.5 79.2
Mariner’s Harbor-Arlington-Graniteville 2020 0.047773 33492 16.8 83.3
Port Richmond 2020 0.044230 22609 22.0 83.2
St. George-New Brighton 2021 0.043798 20549 18.5 79.9
St. George-New Brighton 2017 0.038931 20549 18.5 79.9
Mariner’s Harbor-Arlington-Graniteville 2017 0.038815 33492 16.8 83.3
Tompkinsville-Stapleton-Clifton-Fox Hills 2017 0.036790 19027 25.5 79.2
Rosebank-Shore Acres-Park Hill 2020 0.035280 25510 14.4 81.7
St. George-New Brighton 2022 0.034065 20549 18.5 79.9